What are AI tables? Structure data agents can work with
Tables turn operational information into structured, editable data that people and agents can share. They are useful when a result should become a durable row, field, relation, or view instead of disappearing into chat.
The concept
A plain-language definition before the product implementation.
An AI table is a shared operational data contract
A table gives records a schema: text, numbers, dates, selections, relations, and agent-generated fields. Views let different workflows focus on the same underlying records without duplicating the data.
How AI Agent uses it
Keep structured context next to the work
AI Agent Tables support CSV import, configurable fields, saved views, relations, and agent-computed cells. Agents can query Tables for analysis, draft new tables or fields in chat, and route proposed writes through review where the operation requires it.
Typed fields
Model operational data with clear field types instead of loose prose.
Views and relations
Organize one source of truth for different teams and connect related records.
Agent computation
Use an agent to research or derive a field while retaining the source row.
From idea to a working system.
- 01
Model
Create a table, import CSV data, and define fields and relations.
- 02
Operate
Filter views, edit records, and compute supported fields with agents.
- 03
Investigate
Query structured data from chat, workflows, or Company Brain.
Give repeated records an explicit schema
Best for
Structured rows that agents and people need to filter, enrich, sync, and inspect as shared operational data.
Choose another pattern when
Long-form documents whose useful passages should be retrieved by meaning. Those sources belong in knowledge.
Included in AI Agent
The product capabilities behind the idea.
- CSV import
- Typed fields and saved views
- Relations between tables
- Agent-computed cells
Frequently asked questions
Keep exploring
Related product guides
Company Brain
Query a fresh, read-only analytics copy of your Tables across sources, with freshness and provenance included in successful results.
Chat
Agent chat is the conversational control surface for real work. Ask a question, attach context, choose an agent and skills, then review the evidence, tool activity, and proposed changes in the same thread.
Human approval
Human approval puts a deliberate checkpoint between agent reasoning and a consequential action. The agent can prepare the work, while a person decides whether it should be applied.
Build the smallest useful version first.
Start with the agent and one real task. Add workflow control, context, and approvals when the work shows you where they matter.